






'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : Dict      			=     KandinskyInpaintPipeline
  _a  : Optional[Any]      			=     ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
  _a  : Union[str, Any]      			=     [
      '''prompt''',
      '''negative_prompt''',
      '''image_embeds''',
      '''negative_image_embeds''',
      '''image''',
      '''mask_image''',
  ]
  _a  : List[str]      			=     [
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''negative_prompt''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  _a  : List[str]      			=     False







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 32







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 32







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self.time_input_dim







  @property
  def    _a       (			self	):






         """simple docstring"""


         return self.time_input_dim * 4







  @property
  def    _a       (			self	):






         """simple docstring"""


         return 100







  @property
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'	)
         return tokenizer







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			MCLIPConfig(
             numDims=self.cross_attention_dim   ,	transformerDimensions=self.text_embedder_hidden_size   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	vocab_size=1_005   ,	)

         a_						     =			MultilingualCLIP(UpperCamelCase__	)
         a_						     =			text_encoder.eval()

         return text_encoder







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)

         a_						     =			{
             'in_channels': 9,
             # Out channels is double in channels because predicts mean and variance
             'out_channels': 8,
             'addition_embed_type': 'text_image',
             'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
             'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
             'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
             'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
             'layers_per_block': 1,
             'encoder_hid_dim': self.text_embedder_hidden_size,
             'encoder_hid_dim_type': 'text_image_proj',
             'cross_attention_dim': self.cross_attention_dim,
             'attention_head_dim': 4,
             'resnet_time_scale_shift': 'scale_shift',
             'class_embed_type': None,
         }

         a_						     =			UNetaDConditionModel(**UpperCamelCase__	)
         return model







  @property
  def    _a       (			self	):






         """simple docstring"""


         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
             "in_channels": 3,
             "latent_channels": 4,
             "layers_per_block": 1,
             "norm_num_groups": 8,
             "norm_type": "spatial",
             "num_vq_embeddings": 12,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }







  @property
  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			VQModel(**self.dummy_movq_kwargs	)
         return model







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.dummy_text_encoder
         a_						     =			self.dummy_tokenizer
         a_						     =			self.dummy_unet
         a_						     =			self.dummy_movq

         a_						     =			DDIMScheduler(
             num_train_timesteps=1_000   ,	beta_schedule='linear'   ,	beta_start=0.00_085   ,	beta_end=0.012   ,	clip_sample=UpperCamelCase__   ,	set_alpha_to_one=UpperCamelCase__   ,	steps_offset=1   ,	prediction_type='epsilon'   ,	thresholding=UpperCamelCase__   ,	)

         a_						     =			{
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=0	):






         """simple docstring"""


         a_						     =			floats_tensor((1, self.cross_attention_dim)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			floats_tensor((1, self.cross_attention_dim)   ,	rng=random.Random(seed + 1	)	).to(UpperCamelCase__	)
         # create init_image
         a_						     =			floats_tensor((1, 3, 64, 64)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			image.cpu().permute(0   ,	2   ,	3   ,	1	)[0]
         a_						     =			Image.fromarray(np.uinta(UpperCamelCase__	)	).convert('RGB'	).resize((256, 256)	)
         # create mask
         a_						     =			np.ones((64, 64)   ,	dtype=np.floataa	)
         a_						     =			0

         if str(UpperCamelCase__	).startswith('mps'	):
                a_						     =			torch.manual_seed(UpperCamelCase__	)
         else:
                a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
         a_						     =			{
             'prompt': 'horse',
             'image': init_image,
             'mask_image': mask,
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'generator': generator,
             'height': 64,
             'width': 64,
             'num_inference_steps': 2,
             'guidance_scale': 4.0,
             'output_type': 'np',
         }
         return inputs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'

         a_						     =			self.get_dummy_components()

         a_						     =			self.pipeline_class(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)

         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			pipe(**self.get_dummy_inputs(UpperCamelCase__	)	)
         a_						     =			output.images

         a_						     =			pipe(
             **self.get_dummy_inputs(UpperCamelCase__	)   ,	return_dict=UpperCamelCase__   ,	)[0]

         a_						     =			image[0, -3:, -3:, -1]
         a_						     =			image_from_tuple[0, -3:, -3:, -1]

         print(f'image.shape {image.shape}'	)

         assert image.shape == (1, 64, 64, 3)

         a_						     =			np.array(
             [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]	)

         assert (
             np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
         ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
         assert (
             np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
         ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







  def    _a       (			self	):






         """simple docstring"""


         super().test_inference_batch_single_identical(expected_max_diff=3e-3	)





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'	)

         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'	)
         a_						     =			np.ones((768, 768)   ,	dtype=np.floataa	)
         a_						     =			0

         a_						     =			'a hat'

         a_						     =			KandinskyPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1-prior'   ,	torch_dtype=torch.floataa	)
         pipe_prior.to(UpperCamelCase__	)

         a_						     =			KandinskyInpaintPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1-inpaint'   ,	torch_dtype=torch.floataa	)
         a_						     =			pipeline.to(UpperCamelCase__	)
         pipeline.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			torch.Generator(device='cpu'	).manual_seed(0	)
         a_     ,	a_						     =			pipe_prior(
             UpperCamelCase__   ,	generator=UpperCamelCase__   ,	num_inference_steps=5   ,	negative_prompt=''   ,	).to_tuple()

         a_						     =			pipeline(
             UpperCamelCase__   ,	image=UpperCamelCase__   ,	mask_image=UpperCamelCase__   ,	image_embeds=UpperCamelCase__   ,	negative_image_embeds=UpperCamelCase__   ,	generator=UpperCamelCase__   ,	num_inference_steps=100   ,	height=768   ,	width=768   ,	output_type='np'   ,	)

         a_						     =			output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(UpperCamelCase__   ,	UpperCamelCase__	)












'''simple docstring'''







import cmath
import math


def       __UpperCamelCase					(      lowercase_       :  float   ,       lowercase_       :  float   ,       lowercase_       :  float   ,       lowercase_       :  float   ):
       """simple docstring"""




       a_						     =			math.radians(lowercase_   )
       a_						     =			math.radians(lowercase_   )

       # Convert voltage and current to rectangular form
       a_						     =			cmath.rect(lowercase_   ,       lowercase_   )
       a_						     =			cmath.rect(lowercase_   ,       lowercase_   )

       # Calculate apparent power
       return voltage_rect * current_rect


if __name__ == "__main__":
  import doctest

  doctest.testmod()




